The measurement of serum prostate specific antigen (PSA) is widely used for the screening and early detection of prostate cancer (PCa). As discussed in the public report “Polygenic Risk Score Improves Prostate Cancer Risk Prediction: Results from the Stockholm-1 Cohort Study” by Markus Aly and co-authors as published in EUROPEAN UROLOGY 60 (2011) 21-28 (which is incorporated by reference herein), serum PSA that is measurable by current clinical immunoassays exists primarily as either the free “non-complexed” form (free PSA), or as a complex with a-lantichymotrypsin (ACT). The ratio of free to total PSA in serum has been demonstrated to significantly improve the detection of PCa. Other factors, like age and documented family history may also improve the detection of PCa further. The measurement of genetic markers related to PCa, in particular single nucleotide polymorphisms (SNP), is an emerging modality for the screening and early detection of prostate cancer. Analysis of multiple PCa related SNPs can, in combination with biomarkers like PSA and with general information about the patient improve the risk assessment through a combination of several SNPs into a genetic score.
The screening and early detection of prostate cancer is a complicated task, and to date no single biomarker has been proven sufficiently good for specific and sensitive mapping of the male population. Therefore, attempts have been spent on combining biomarker levels in order to produce a formula which performs better in the screening and early detection of PCa. The most common example is the regular PSA test, which in fact is an assessment of “free” PSA and “total” PSA. PSA exists as one “non-complex” form and one form where PSA is in complex formation with alpha-lantichymotrypsin. Another such example is the use of combinations of concentrations of free PSA, total PSA, and one or more pro-enzyme forms of PSA for the purpose of diagnosis, as described in WO03100079 (METHOD OF ANALYZING PROENZYME FORMS OF PROSTATE SPECIFIC ANTIGEN IN SERUM TO IMPROVE PROSTATE CANCER DETECTION) which is incorporated by reference herein. The one possible combination of PSA concentrations and pro-enzyme concentrations that may result in improved performance for the screening and early detection of PCa is the phi index. Phi was developed as a combination of PSA, free PSA, and a PSA precursor form [−2]proPSA to better detecting PCa for men with a borderline PSA test (e.g. PSA 2-10 ng/mL) and non-suspicious digital rectal examination, as disclosed in the report “Cost-effectiveness of Prostate Health Index for prostate cancer detection” by Nichol M B and co-authors as published in BJU Int. 2011 Nov. 11. doi: 10.1111/j.1464-410X.2011.10751.x. which is incorporated by reference herein. Another such example is the combination of psp94 and PSA, as described in US2012021925 (DIAGNOSTIC ASSAYS FOR PROSTATE CANCER USING PSP94 AND PSA BIOMARKERS).
There are other biomarkers of potential diagnostic or prognostic value for assessing if a patient suffers from PCa, including MIC-1 as described in the report “Macrophage Inhibitory Cytokine 1: A New Prognostic Marker in Prostate Cancer” by David A. Brown and co-authors as published in Clin Cancer Res 2009; 15(21):OF1-7, which is incorporated by reference herein.
Attempts to combine information from multiple sources into one algorithmic model for the prediction of PCa risk has been disclosed in the past. In the public report “Blood Biomarker Levels to Aid Discovery of Cancer-Related Single-Nucleotide Polymorphisms: Kallikreins and Prostate Cancer” by Robert Klein and co-authors as published in Cancer Prev Res (2010), 3(5):611-619 (which is incorporated by reference herein), the authors discuss how blood biomarkers can aid the discovery of novel SNP, but also suggest that there is a potential role for incorporating both genotype and biomarker levels in predictive models. Furthermore, this report provides evidence that the non-additive combination of genetic markers and biomarkers in concert may have predictive value for the estimation of PCa risk. Later, Xu and co-inventors disclosed a method for correlating genetic markers with high grade prostate cancer, primarily for the purpose of identifying subjects suitable for chemopreventive therapy using 5-alpha reductase inhibitor medication (e.g. dutasteride or finasteride) in the patent application WO2012031207 (which is incorporated by reference herein). In concert, these two public disclosures summarizes the prior art of combining genetic information and biomarker concentration for the purpose of estimating PCa risk, also for high grade cancers.
The current performance of the PSA screening and early detection is approximately a sensitivity of 80% and specificity of 30%. It is estimated that approximately 65% will undergo unnecessary prostate biopsy and that 15-20% of the clinically relevant prostate cancers are missed in the current screening. In the United States alone, about 1 million biopsies are performed every year, which results in about 192 000 new cases being diagnosed. Hence, also a small improvement of diagnostic performance will result both in major savings in healthcare expenses due to fewer biopsies and in less human suffering from invasive diagnostic procedures.
The current clinical practice (in Sweden) is to use total PSA as biomarker for detection of asymptomatic and early prostate cancer. The general cutoff value for further evaluation with a prostate biopsy is 3 ng/mL. However, due to the negative consequences of PSA screening there is no organized PSA screening recommended in Europe or North America today.
It is particularly important to accurately identify aggressive prostate cancer (aPCa) in individuals because the sooner an individual is provided treatment, the greater likelihood of the cancer being cured. The identification of aPCa is however difficult, partly because larger cohorts are required to provide a sufficient number of cases and controls in the development of statistical models. Hence, the availability of predictive models for aPCa is low. This invention provides, however, predictive models for the identification of aPCa through analysis of biomarkers and genetic profile of an individual.